ABSTRACT There are an estimated 1.5 million U.S. workers occupationally exposed to ionizing radiation each year. Of this number, approximately 120,000 workers are monitored annually for radiation exposure at United States Department of Energy facilities, while hundreds of thousands of others are exposed while working with medical sources of radiation, nuclear power generation, and industrial processes such as radiography and food irradiation. In contrast to many other established occupational carcinogens, which have been removed from US workplaces over time, the number of radiation-exposed workers has not diminished, but rather has grown with the emergence of new uses of radiation in medicine and other industrial settings. Our understanding of the health effects of radiation exposure comes from a variety of sources, including laboratory research, studies of medical irradiation, and studies of atomic bomb survivors. However, epidemiological studies of workers hold a special place because such studies allow direct evaluation of evidence that does not require extrapolation from cells to organisms, between species, or across populations and exposure conditions. Until recently, epidemiological studies of radiation workers tended to result in imprecise risk estimates with confidence intervals that often spanned the null. Consequently, epidemiological analyses of Japanese atomic bomb survivors have served as the primary quantitative basis for radiation protection standards. However, recent epidemiological studies that pool cohort data have yielded radiation risk estimates with relatively tight confidence intervals. To strengthen the basis for protection of contemporary radiation workers, and to improve compensation decisions for workers exposed in the past, we propose state-of-the-art statistical analysis using parametric g-formula methods applied to data that recently have been assembled as part of a major international effort to pool data for nuclear workers employed in the United Kingdom, France, and USA. Specifically, we propose to assess: 1) temporal modifiers of radiation effects (time-since-exposure, age-at- exposure, and attained age); 2) variation in radiation effects by type of cancer; and, 3) radiation effects on non- cancer causes of death. In addition, we propose Bayesian methods to evaluate 4) dose and dose-rate effects; and, 5) bias due to outcome misclassification. Because a causal interpretation of epidemiological findings is strengthened by evidence of reproducibility and consistency, we will assess the consistency of results derived from these international cohorts. The proposed methods allow us to minimize bias, including healthy worker survivor bias, formally combine information from different studies, and leverage these recently pooled nuclear worker cohort data. The findings of this research project are expected to have substantial impact on understanding of the effects occupational radiation exposures. The work will address NORA priorities by improving understanding of cancer caused by radiation and strengthening the basis for policy recommendations. 1